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Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential (“risky” stomatal regulation), high xylem hydraulic conductivity, and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance, and low stomatal conductance at low leaf water potential (“conservative” stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits and their interactions (i.e., networks), has potential to improve crop growth predictions in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.Summary statementOur process-based model uncovered two beneficial but contrasting trait networks for maize which can be understood by their integrated effect on water use/conservation. Modification of multiple, physiologically aligned, traits were required to bring about meaningful improvements in NPP and yield.

Growing competition for water is incentivizing the implementation of deficit irrigation. Thus, there is a need to accurately map actual crop evapotranspiration (ETa) to more efficiently manage and document irrigation. An alternative is the use of remote sensing (RS) platforms. Unmanned Aerial Systems (UAS) can fly frequently and acquire very high spatial resolution images. Multispectral UASs (fixed-wing and multi-rotor) flew over irrigated corn fields, in northern Colorado, to evaluate the capabilities of the RS systems on irrigation management. Soil water content sensors were used in the evaluation. Using multispectral UAS platforms in irrigation management is advantageous in regards to having the capability to assess crop water use and stress frequently and at very high spatial resolutions. This study shows that inferring crop water use and soil water status with acceptable errors is possible with visible-near-infrared and thermal cameras. Furthermore, the required imagery processing and calibration is detailed.

Improved irrigation management depends on accurate irrigation scheduling that should be based on estimates of daily crop water requirements. Traditional methods for estimating daily crop water requirements use a generic crop coefficient (Kc) that is based on point measurement and is developed for crops growing under optimal conditions that do not reflect actual growing conditions in regular fields. Therefore, daily water requirements based on these coefficients are not always accurate, require using stress coefficients, and obtaining crop coefficients under actual growing condition using traditional point measurement approaches, such as lysimeters or soil moisture monitoring, is impractical. Surface energy balance models can be used to directly estimate actual evapotranspiration (ET) in many fields, and these estimates can be used to develop regional crop coefficients that reflect the actual growing conditions. The objective of this study is to develop regional remote sensing–based corn Kcs for th...

A semianalytical method commonly used for quantifying stream depletion caused by ground water pumping was reviewed for applicability in narrow alluvial aquifers. This stream depletion factor (SDF) method is based on the analytic Glover model, but uses a numerical model-derived input parameter, called the SDF, to partly account for mathematically nonideal conditions such as variable transmissivity and nearby aquifer boundaries. Using the SDF can improve and simplify depletion estimates. However, the method's approximations introduce error that increases with proximity to the impermeable aquifer boundary. This article reviews the history of the method and its assumptions. New stream depletion response curves are presented as functions of well position within bounded aquifers. A simple modification to modeled SDF values is proposed that allows the impermeable boundary to be accounted for with image wells, but without overaccounting for boundary effects that are already reflected in modeled SDFs. It is shown that SDFs for locations closer to the river than to the aquifer boundary do not reflect impermeable-boundary effects, and thus need no modification, and boundary effects in the other portion of the aquifer follow a predictable removable pattern. This method is verified by comparing response curves using modified SDFs with response curves from an extensively calibrated numerical model of a managed ground water recharge site. The modification improves SDF-based stream depletion estimates in bounded aquifers while still benefiting from the additional information contained in SDF maps and retaining their value as standardized references for water rights administration.